Leveraging experience for computationally efficient adaptive nonlinear model predictive control

Abstract

This work presents Experience-driven Predictive Control (EPC) as a fast technique for solving nonlinear model predictive control (NMPC) problems with uncertain system dynamics. EPC leverages an affine dynamics model that is updated online via Locally Weighted Projection Regression (LWPR) to capture nonlinearities, uncertainty, and changes in the system dynamics. This model enables the NMPC problem to be re-cast as a quadratic program (QP). The QP can then be solved via multi-parametric techniques to generate a mapping from state, reference, and dynamics model to a locally optimal, affine feedback control law. These mappings, in conjunction with the basis functions learned via LWPR, define a notion of experience for the controller as they capture the full input-output relationship for previous actions the controller has taken. The resulting experience database allows EPC to avoid solving redundant optimization problems, and as it is constructed online, enables the system to operate more efficiently over time. We demonstrate the performance of EPC through a set of hardware-in-the-loop simulation studies of a quadrotor micro air vehicle that is subjected to unmodeled exogenous perturbations.